Background
Urinary problems, particularly when accompanied with urinary incontinence (UI), have been shown to significantly impact different domains of health-related quality of life (HRQoL) such as emotional well-being, performance of daily activities and social interaction [
1], and have also been associated with economic burden [
2],[
3] and lower productivity [
1],[
4]. Neurogenic detrusor overactivity (NDO) is an etiology of UI that is caused by conditions such as multiple sclerosis (MS) or spinal cord injury (SCI). Detrusor overactivity is an involuntary bladder contraction during the filling phase of cystometry [
5]. As a result of a disruption in the regulation of the micturition reflex, NDO patients frequently suffer from urinary symptoms including urgency and urinary urgency incontinence, which negatively affect their HRQoL [
6],[
7].
A practical approach to evaluating the health states derived from a disease is through administration of existing generic preference-based instruments such as the EQ-5D [
8],[
9], the Health Utility Index –Mark 2 or Mark 3- (HUI2 or HUI 3, respectively) [
10],[
11] or the SF-6D [
12],[
13]. These instruments are suitable across patient populations, regardless of the disease, allowing investigators to describe and compare important aspects of HRQoL and produce preference-based or utility scores. Although there is evidence to suggest that there is an underlying basic construct measured by the three generic instruments, it is well established that they produce different values and are not interchangeable [
14]-[
18]. Furthermore, there is controversy about their discriminative ability and sensitivity to detect clinically important changes in varying patient populations and consequently, these measures may not be the best choice for certain conditions [
19], including urinary incontinence-related problems [
20]-[
23].
There are a number of condition-specific instruments available for patients with lower urinary tract symptoms and UI. Some commonly-used measures in clinical trials and outcomes research are the Overactive Bladder Questionnaire (OAB-q) [
24], the King's Health Questionnaire (KHQ) [
25] and the Incontinence Quality of Life Questionnaire (I-QOL) [
26]-[
28]. These instruments have good psychometric properties in terms of reliability, construct and discriminant validity, and responsiveness [
6],[
24],[
27],[
29]. Recently, new efforts have been focused on estimating utilities related to health states derived from these tools by means of surveying different samples of patients or general population from Europe and the US [
30]-[
32]. However, out of all these measures, only the I-QOL questionnaire includes a specific module for NDO patients developed from the needs-based model [
26],[
27]. In addition, the validity of the I-QOL has been demonstrated in patients with neurogenic urinary incontinence [
6]. Consequently, the overall aim of this research was to generate a preference-based measure from the I-QOL and its neurogenic set, the Incontinence Utility Index (IUI), by means of surveying a representative sample of the general population. This new instrument would represent a more comprehensive measure for valuing health states associated with urinary problems from a range of different etiologies.
Results
I-QOL reduction
Outputs from Rasch analysis are presented in Table
1. No age related DIF was identified. Items 1, 3, 4 and 10 in the I-QOL and 2 and 4 in the Neurogenic Module had etiology related DIF. Item 10 from the I-QOL and 2 and 4 from the Neurogenic Module had sex related DIF. These six items were removed from the selection based exclusively on the results of the Rasch analysis. Additionally, as previously stated, an expert panel then proceeded to consider the results of the analysis jointly with the item content, to reach the final selection of 5 items considered to represent a set of complementary attributes. The 5 response categories were collapsed into 3 to simplify health state valuation, yielding the abbreviated health state classification system (Table
2). This final version proved to be internally consistent and valid for NDO patients according to the psychometric analyses presented in Table
3: at week 6, the abbreviated health state classification proved to have adequate ability to detect changes in those patients who showed a reduction in incontinence episodes (responders), and the association between daily incontinence episodes and health state classification scores was considered adequate. Furthermore, the level of agreement between the original I-QOL and the abbreviated health classification system was high (ICC = 0.90; 95% CI: 0.89-0.91) and statistically significant (p < 0.001).
Table 1
Summary of Rasch outputs
I-QOL01 | 0.17 | 1.04 | 1.12 | 0.48 |
0.60
|
0.000
|
−0.47
|
0.000
|
I-QOL02 | −1.07 | 1.25 | 1.33 | 0.50 | 0.23 | 0.002 | −0.29 | 0.000 |
I-QOL03 | −0.85 | 1.23 | 1.32 | 0.51 |
0.59
|
0.000
| −0.41 | 0.000 |
I-QOL04 | 0.26 | 1.05 | 1.19 | 0.47 |
0.60
|
0.000
| −0.40 | 0.000 |
I-QOL05 | −0.29 | 0.97 | 1.01 | 0.55 | −0.25 | 0.001 | 0.10 | 0.209 |
I-QOL06 | −0.15 | 0.88 | 0.83 | 0.59 | 0.00 | 1.000 | 0.14 | 0.060 |
I-QOL07 | 0.22 | 0.74 | 0.70 | 0.61 | −0.04 | 0.595 | 0.00 | 1.000 |
I-QOL08 | −0.32 | 1.09 | 1.12 | 0.51 | −0.35 | 0.000 | 0.27 | 0.000 |
I-QOL09 | 0.51 | 0.75 | 0.74 | 0.60 | 0.16 | 0.056 | −0.05 | 0.557 |
I-QOL10 | 0.22 | 0.94 | 0.97 | 0.52 |
0.76
|
0.000
|
−0.53
|
0.000
|
I-QOL11 | 0.25 | 0.86 | 0.83 | 0.56 | −0.21 | 0.013 | 0.19 | 0.029 |
I-QOL12 | 0.27 | 1.01 | 1.10 | 0.49 | 0.18 | 0.025 | −0.09 | 0.230 |
I-QOL13 | −0.12 | 1.10 | 1.12 | 0.49 | 0.33 | 0.000 | −0.19 | 0.010 |
I-QOL14 | 0.44 | 0.72 | 0.68 | 0.61 | 0.00 | 1.000 | −0.03 | 0.761 |
I-QOL15 | −0.29 | 0.92 | 0.92 | 0.58 | 0.12 | 0.103 | 0.00 | 1.000 |
I-QOL16 | −0.46 | 0.82 | 0.81 | 0.62 | −0.06 | 0.432 | 0.00 | 1.000 |
I-QOL17 | −0.12 | 0.75 | 0.72 | 0.63 | 0.00 | 1.000 | 0.00 | 1.000 |
I-QOL18 | 0.76 | 0.75 | 0.69 | 0.57 | −0.22 | 0.017 | 0.00 | 1.000 |
I-QOL19 | 0.91 | 0.95 | 0.82 | 0.48 | −0.20 | 0.054 | 0.13 | 0.195 |
I-QOL20 | 0.46 | 0.94 | 0.92 | 0.51 | −0.06 | 0.521 | −0.08 | 0.376 |
I-QOL21 | −0.45 | 0.98 | 0.96 | 0.57 | 0.13 | 0.075 | −0.11 | 0.132 |
I-QOL22 | −0.47 | 1.24 | 1.37 | 0.48 | −0.43 | 0.000 | 0.29 | 0.000 |
Neurog1† | −0.26 | 1.20 | 1.29 | 0.47 | 0.00 | 1.000 | 0.00 | 1.000 |
Neurog2 | −0.23 | 1.33 | 1.61 | 0.42 |
−0.61
|
0.000
|
0.57
|
0.000
|
Neurog3 | 0.95 | 1.06 | 0.99 | 0.42 | −0.24 | 0.025 | 0.18 | 0.099 |
Neurog4 | −0.51 | 1.56 | 1.85 | 0.39 |
−0.57
|
0.000
|
0.51
|
0.000
|
Neurog5 | 0.15 | 1.14 | 1.16 | 0.46 | −0.11 | 0.128 | −0.12 | 0.096 |
Table 2
The abbreviated health state classification system derived from the I-QOL and Neurogenic Module
I-QOL Item5: Depression | I feel not at all depressed because of my urinary problems or incontinence | 5 |
I feel somewhat depressed because of my urinary problems or incontinence | 3 |
I feel extremely depressed because of my urinary problems or incontinence | 1 |
I-QOL Item8: Urine Smell | I do not worry at all about other people smelling urine on me | 5 |
I worry somewhat about other people smelling urine on me | 3 |
I worry a very great deal about other people smelling urine on me | 1 |
I-QOL Item13: Sleep | I have no difficulty getting a good night's sleep because of my urinary problems or incontinence | 5 |
I have some difficulty getting a good night's sleep because of my urinary problems or incontinence | 3 |
I have extreme difficulty getting a good night's sleep because of my urinary problems or incontinence | 1 |
I-QOL Item19: Bladder Control | I feel I have control over my bladder | 5 |
I feel I have some control over my bladder | 3 |
I feel I have no control over my bladder | 1 |
I-QOL Item20: Drinks | I have to be not at all careful about what or how much I drink because of my urinary problems or incontinence | 5 |
I have to be somewhat careful about what or how much I drink because of my urinary problems or incontinence | 3 |
I have to be extremely careful about what or how much I drink because of my urinary problems or incontinence | 1 |
Table 3
Psychometric properties of the abbreviated health state classification system in neurogenic detrusor overactivity patients
Psychometric properties*
|
Criterion validity: differences in the scale score of the abbreviated health state classification system (0: worst health status – 100: best health status) according to the reduction in the frequency of urinary incontinence episodes at week 6)
|
|
N
|
Mean
|
95% CI Lower L.
|
95% CI Upper L.
|
p
|
Scale score at Day 1 | Reduction <50% | 225 | 35.11 | 32.73 | 37.49 | 0.242 |
50% < = Reduction <100% | 224 | 35.04 | 32.75 | 37.34 |
100% Reduction | 188 | 37.93 | 35.40 | 40.45 |
Scale score at Week 6 | Reduction <50% | 222 | 38.69 | 36.14 | 41.25 | <0.001 |
50% < = Reduction <100% | 224 | 54.33 | 54.62 | 57.04 |
100% Reduction | 186 | 70.81 | 67.72 | 73.89 |
Convergent validity Spearman correlation coefficient
|
| |
Daily Incontinence episodes
| | |
Scale score at Day 1 | | −0.22 | | <0.01 |
Scale score at Week 6 | | −0.43 | | |
Weighting the health states derived from the I-QOL
Complete descriptions of the multi-attribute health states and the sample are presented in Tables
4 and
5. A total of 442 interviews were completed, however, 44 cases were withdrawn because they presented at least one inconsistency in their ratings: if VAS values for a given corner state (health states A to E, Table
4) were lower than the VAS values of comparable marker states (M1 to M3, Table
4), n = 50 (please note some participants provided more than one inconsistent answer); or if the value of any corner or marker states were lower than the VAS value of the least desirable health state, n = 24. Only those participants successfully completing all the rating exercises were included, n = 398, generating a total of 2,388 TTO evaluations. With respect to interview quality, 97.7% were performed with full cooperation of the respondent, 84.7% of participants thought carefully before answering, and 84.9% experienced very little or no problems completing the survey. Moreover, mean time required to complete the survey was 30.2 minutes (Standard deviation -SD- 10.9) and the vast majority of interviewers rated the quality as good or very good (94.2%) with less than 1% of interviews being considered of inferior quality.
Table 4
Multi-attribute health states used in preferences elicitation
A† | 3 | 1 | 1 | 1 | 1 |
B | 1 | 3 | 1 | 1 | 1 |
C† | 1 | 1 | 3 | 1 | 1 |
D† | 1 | 1 | 1 | 3 | 1 |
E | 1 | 1 | 1 | 1 | 3 |
M1† | 1 | 2 | 3 | 1 | 2 |
M2† | 3 | 1 | 1 | 2 | 2 |
M3† | 1 | 2 | 1 | 3 | 3 |
P | 1 | 1 | 1 | 1 | 1 |
W | 3 | 3 | 3 | 3 | 3 |
Dead | -- | -- | -- | -- | -- |
Table 5
Description of participants in the elicitation survey (valid cases, n= 398)
Gender | Female | 239 | 60.1 |
Region | North East | 20 | 5.0 |
North West | 38 | 9.5 |
Yorkshire and The Humber | 35 | 8.8 |
Midlands | 55 | 13.8 |
East of England | 32 | 8.0 |
London | 47 | 11.8 |
South East | 68 | 17.1 |
South West | 37 | 9.3 |
Scotland | 36 | 9.0 |
Northern Ireland | 10 | 2.5 |
Wales | 20 | 5.0 |
Education | Some secondary school | 14 | 3.5 |
GCSE or equivalent | 71 | 17.8 |
`A' level or equivalent | 71 | 17.8 |
Diploma or certificate of higher education | 66 | 16.6 |
Bachelor's degree or equivalent | 107 | 26.9 |
Master's or Doctoral degree/Post graduate certificate | 69 | 17.3 |
Employment status | Working full-time | 150 | 37.7 |
Working part-time | 56 | 14.1 |
Not working | 55 | 13.8 |
Looking for work | 4 | 1.0 |
Student | 36 | 9.0 |
Retired | 65 | 16.3 |
Self employed | 32 | 8.0 |
Age | Mean (SD) | 44.75 | 14.6 |
Groups of Age | 18-29 | 73 | 18.3 |
30-39 | 92 | 23.1 |
40-49 | 90 | 22.6 |
50-59 | 73 | 18.3 |
60-69 | 54 | 13.6 |
>70 years old | 16 | 4.0 |
Suffering a Chronic Disease | Yes | 126 | 31.7 |
Acute disease | Yes | 35 | 8.8 |
Bladder symptoms | Yes | 145 | 36.4 |
Bladder symptoms in family or friends | Yes | 191 | 48.0 |
A majority of respondents were female (60.1%), mean age was 44.75 years (SD14.6); 60.8% had at least a diploma education (2 years of college) and a similar percentage were employed (59.8%). With respect to their health status, 31.7% reported a chronic illness and 8.8% an acute disease. Regarding previous experience, 36.4% declared they had suffered symptoms associated with OAB or UUI and 48.0% recognized some of these problems in their relatives or friends.
With respect to participants' preferences about the worst state described by the abbreviated health state classification system and dead, most of them (n = 294, 73.9%) stated they would prefer living the next 30 years in health state W (Group B), while the rest (n = 104, 26.1%) preferred being dead to living in health state W (Group A).
MAUF estimation and final algorithm of the Incontinence Utility Index
Trimmed values (10%) were fitted separately for each group based on power functions (Equation 3 in Additional file
1) and natural log transformations (Equation 4 in Additional file
1) to convert mean VAS (v) into utility scores (u). Regression models yielded good fit (R
2 group A = 0.923 and R
2 group B = 0.978) and power functions resulted as follows: Group A, u = 1-(1-v)
1.229 and Group B, u = 1-(1-v)
0.841. Estimates of the relative weight of each attribute fitted in the perfect health = 0 and worst state = 1 for Group A were: c1 = 0.393, c2 = 0.450, c3 = 0.387, c4 = 0.562 and c5 = 0.283 (Σcj = 2.076; c = −0.911). For Group B: c1 = 0.636, c2 = 0.640, c3 = 0.616, c4 = 0.775 and c5 = 0.490 (Σcj = 3.158; c = −0.994). From these results it was seen that the multiplicative form was an appropriate form.
Final utilities were calculated based on the prevalence proportion in Person-Mean A and Person-Mean B groups (both in W = 0.00/P = 1.00 scale): uj = (104* Person-Mean A uj + 294 * Person-Mean B uj -re-scaled-)/398. A positive linear transformation was applied to re-scale the utilities into a dead = 0.00 / P = 1.00 scale to facilitate comparisons with other utility measures. Table
6 shows utility weights estimated for the multi-attribute health states defined in Table
4. The disutility weights estimated for each attribute from the overall sample were: c1 = 0.470, c2 = 0.484, c3 = 0.456, c4 = 0.590 and c5 = 0.358 (Σcj = 2.357; c = −0.951). Once again the results rejected the linear additive form and showed that all attributes were preference complements. The five single attribute utility coefficients and the overall MAUF are presented in Table
7 with possible scores ranging from 0.036 (worst health state) to 1 (perfect health).
Table 6
Estimated overall utility scores
A | 398 | 0.427 | 0.530 |
B | 398 | 0.409 | 0.516 |
C | 398 | 0.444 | 0.544 |
D | 398 | 0.280 | 0.410 |
E | 398 | 0.564 | 0.642 |
M1 | 398 | 0.259 | 0.392 |
M2 | 398 | 0.243 | 0.379 |
M3 | 398 | 0.185 | 0.331 |
P | 398 | 1 | 1 |
D | 398 | −0.219 | 0 |
W | 398 | −0.037 | 0.150 |
Table 7
Single and Multi-attribute utilities
1 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
2 | 0.600 | 0.457 | 0.613 | 0.627 | 0.655 |
3 | 0.178 | −0.034 | 0.178 | 0.178 | 0.178 |
Final Multi-attribute utility function coefficients (p = 0.051)
|
Level
|
Depression (w1)
|
Urine Smell (w2)
|
Sleep (w3)
|
Bladder Control (w4)
|
Drinks (w5)
|
1 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
2 | 0.821 | 0.750 | 0.832 | 0.791 | 0.883 |
3 | 0.633 | 0.524 | 0.644 | 0.539 | 0.721 |
Final algorithm: u* = 1.051 (w1 * w2 * w3 * w4 * w5) - 0.051
|
Predictive validity of the MAUF
Mean utility scores of marker states directly elicited on the TTO were compared against those estimated by the MAUF to test its predictive validity. The results were as follows: Σ differences = −0.038; MD = −0.013; MAD = 0.038; OSD = 0.004 and ICC (95% CI) = 0.928 (0.648-0.985). Thus, the calculated MAUF showed a very slight tendency to underpredict directly elicited utilities. Moreover, the level of agreement found between both methods (ICC) was good and only 7.2% of variability could not be attributed to subjects.
Discussion
In this study, a new utility index, the IUI, has been estimated from the abbreviated health state classification system derived from I-QOL and its neurogenic module by means of eliciting preferences from a representative sample of UK adult general population [
50]. The abbreviated I-QOL version was internally consistent and able to capture clinically important differences in clinical status of NDO patients with UI (i.e. changes in HRQoL according to reductions in the average number of IU episodes per week). Furthermore, a high level of agreement was found between the reduced version and the original I-QOL, confirming the appropriateness of the abbreviated health state classification system of 5 domains and its modelling space for utility estimation. Moreover, all the psychometric procedures undertaken to reduce the I-QOL have been successfully applied previously [
30],[
36],[
51] and have been recently recommended [
34].
Regarding the elicitation process, methods applied are consistent with those used to develop one of the most widespread and robust generic utility measures, the HUI [
10],[
11]. As has occurred in previous publications, the additive model was rejected in this study [
10],[
11],[
42] and attributes were preference complements: for instance, the perceived limitation associated with being depressed and not having bladder control is greater than the separate effect of being depressed and not having bladder control, but smaller than the sum of these two problems.
In addition, predictive validity of IUI scoring algorithm was confirmed after comparing the direct utility values and those estimated for the final algorithm. Recognizing that IUI algorithm showed a slight tendency to underpredict the directly elicited utilities, error size was small and comparable to those errors reported for other utility instruments [
11]. What is more, the ICC between direct and indirect values showed an adequate level of agreement.
Generic preference-based indices have historically been the most commonly used means of estimating utilities across a variety of conditions. However, substantial research has been conducted which shows the limitations of these instruments in different conditions [
19]-[
22], as well as the lack of concordance between the utility values obtained from their application [
14]-[
16],[
18],[
52],[
53]. As a result, the development of condition-specific preference-based measures has been gaining ground in recent years [
30]-[
32],[
54].
There are published studies focused on obtaining utility scores from condition-specific instruments for urinary problems. An algorithm has been generated to derive utilities from the KHQ by eliciting preferences from a sample of UI patients [
31]. Kay et al. (2013) [
32] mapped EQ-5D utility scores from the I-QOL among patients with neurogenic and idiopathic OAB using cross-sectional data from Europe and the US. Finally, Yang et al. estimated a population's preference-based index from the OAB-q, the OAB-5D [
30]. Consequently, although the IUI was derived from the I-QOL and its specific module for neurogenic patients, the OAB-5D is the most similar instrument because its modelling space was also obtained from applying Rasch, preference elicitation involved TTO evaluations, and also incorporates general population preferences. Nevertheless, relevant differences lay in the characteristics of the samples used in the reduction process (we specifically used NDO patients) and in the estimation models applied to derive the utility scores since OAB-5D followed the methods described previously for the SF-6D [
12] and we computed a MAUF in accordance with the HUI latest versions [
10],[
11]. Despite these differences, mean absolute error/differences in both measures are comparable (OAB-5D: 0.044 versus IUI: 0.038). Hence, additional research is needed to compare performance of each respective measure in the same populations (i.e. criterion validity, responsiveness and influence on cost-effectiveness ratios).
Despite the fact that the MAUF has proven robust, there are a number of limitations in this research. It should be noted that we used TTO evaluations instead of the Standard Gamble (SG). Although SG is considered the preferred technique to collect subjects' preferences, TTO is a legitimate and extensively used technique, generally considered easier to understand and less time consuming [
30],[
55]. Preferences were elicited from a UK-specific population, so caution should be used before applying the algorithm to other countries, especially if the population is expected to perceive urinary problems differently. Additionally, as a condition-specific preference-based measure, the IUI may suffer from some potential risks in terms of comparability of results [
34]. The risk of
focusing effects (i.e. cognitive bias that occurs when participants place too much importance on the problems associated with the health states presented to them compared with other conditions) was obviated as best as possible by clearly stating throughout the preference elicitation process that, apart from the health states described by the reduced version of the I-QOL, other important aspects of life (i.e. family, economic situation, friends, job, etc.) would remain constant.
Another source of limitations referred to as
anchoring (defining a specific upper anchor that could make comparability across other preference-based instruments problematic) was also anticipated. Consequently, the upper limits during the evaluation process were defined as the most desirable health state, the best health state imaginable, or full health to best facilitate comparisons with other scales. Finally, while the 30-year time horizon was set to illustrate the chronicity of health states, this time frame may not have been the most appropriate for participants under 30 years of age (18.3%) or, particularly for those older than 60 years (17.6%). Thus, this time horizon may result in some over/underestimations during TTO exercises with these subsamples [
56],[
57].
Conclusions
The I-QOL and the IUI are valid-in-population measures for measuring HRQoL and utilities, respectively, associated with urinary problems. Although the IUI is the first utility measure that has been developed for a specific subset of patients with urinary symptoms (NDO population), it is important to note that the final selection of attributes included in the IUI is from the original I-QOL, with no items utilized from the Neurogenic Module. Hence, investigators may test its applicability in other relevant subsamples. It is worth noting that the use of a representative sample of general population to value its health states may ease the application of this instrument in new subsets of patients suffering from urinary problems. New research is currently underway to confirm the soundness of the IUI modelling space on idiopathic OAB patients and to study the responsiveness and the minimally important differences of the IUI in both NDO and idiopathic OAB populations. These insights will be of value to future researchers using the IUI instrument which is intended to complement utility estimates provided by generic instruments to support decision-making with reliable, valid and understandable information presented on a similar scale.
Competing interests
Financial support for this study was provided entirely by a contract with Allergan, Inc. The funding agreement ensured the authors' independence in designing the study, interpreting the data, writing, and publishing the report. The following authors were employed by the sponsor during the writing of this manuscript: KK, CW and DG. At the time of submission, CW and DG were not employed at Allergan, Inc. Furthermore, according to the purposes and scope of this work, authors hereby declare no other financial conflict of interest.
Authors' contributions
JC, NC designed the two phases of the study, coordinated the study, conducted and reviewed the statistical analysis and drafted the manuscript. DLP supervised the design of the study, critically reviewed the content of the reduced version of the I-QOL and the statistical reports and the manuscript. KK, CW and DG, participated in the design and coordination of this research, supervised the statistical analyses in both phases and collaborated drafting and critically reviewing the manuscript. All authors revised and approved this final version of the manuscript.